The number of images and other types of media content that are available to users via their computers, especially with the evolvement of the Internet, has become very large and is continuing to grow daily. For instance, people often download media content such as multimedia files, images, videos, audio, and so on from the World Wide Web (WWW). Additionally, a number of known computer programs simplify user generation of personalized media files. Moreover, multimedia files are often used to enhance documents and are typically distributed via e-mail as attachments.
It is very difficult to manage and utilize large and dynamic sets of media content or multimedia data (e.g., media from a web page, an email attachment, a multimedia generation tool, and so on.) once it is accessed or saved into a user's computing environment. For instance, once such data are saved into local folders, substantial numbers of accumulated multimedia files are typically never used again because they are difficult for the user to locate (e.g., through a search). This is often the case because media files themselves may be stored in an ad-hoc manner.
One conventional technique to facilitate a user's explicit search for media content requires the manual annotation of media content to identify semantics of the media. This technique is substantially limited for a number of reasons. One problem with this conventional technique to identify image semantics is that an image must be manually annotated prior to the user's actual search for media content corresponding to the image. Another problem with this technique is that manually annotating multimedia to include text is a tedious process that is prone to human subjectivity and error. In other words, what one person may consider to be semantically related (e.g., the subject matter, pertinent, interesting, significant, and so on) to a particular image may be quite different from what another person may consider to be semantically related to the particular image.
Another conventional technique to facilitate a user's explicit search for media content analyzes text on a Web page text to identify semantics of images displayed on the page. This analyzed text is compared to the user's search query. If it matches to some extent, then the Web page may include media that is related to the user's search. This technique is substantially limited in that images on a Web page may have semantics other than what is specifically recited with the text on the Web page.
The following arrangements and procedures address these and other problems of managing and accessing multimedia data.